IDD is a novel dataset for road scene understanding in unstructured environments. It consists of 10,000 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes.

The dataset consists of images obtained from a front facing camera attached to a car driven in Hyderabad and Bengaluru. The images are mostly of 1080p resolution, but there are also some images with 720p and other resolutions. In addition to enabling researchers develop algorithms for the unique Indian conditions, this dataset also provides an opportunity for the global research community to investigate emerging AI concepts and benchmark their solutions.

IDD- Temporal

Temporally nearby frames (+/- 15 frames) from the IDD Segmentation data

IDD — Detection

40,000 images with bounding box annotations; released 2018

IDD — Lite

Subsampled version for IDD for use in resource constrained training/deployment, architecture search; 50MB in size with 7 classes

IDD — Multimodal

Primary, Secondary and Supplemental download packages containing (i) stereo images from front camera at 15 fps, (ii) GPS points at 15 Hz – latitude & longitude, and (iii) 16-channel LIDAR and (iv) OBD data

IDD- Segmentation

20,000 images and fine semantic segmentation annotation (14K Train, 2K Val, 4K Test) from 350 drive sequences